Prediction Model for Tumor Budding Status Using the Radiomic Features of F-18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Cervical Cancer
Objective: To compare the radiomic features of F-18 fluorodeoxyglucose positron emission tomography/computed tomography (<sup>18</sup>F-FDG PET/CT) and intratumoral heterogeneity according to tumor budding (TB) status and to develop a prediction model for the TB status using the radiomic...
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MDPI AG
2021-08-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/11/8/1517 |
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| author | Gun Oh Chong Shin-Hyung Park Shin Young Jeong Su Jeong Kim Nora Jee-Young Park Yoon Hee Lee Sang-Woo Lee Dae Gy Hong Ji Young Park Hyung Soo Han |
| author_facet | Gun Oh Chong Shin-Hyung Park Shin Young Jeong Su Jeong Kim Nora Jee-Young Park Yoon Hee Lee Sang-Woo Lee Dae Gy Hong Ji Young Park Hyung Soo Han |
| author_sort | Gun Oh Chong |
| collection | DOAJ |
| description | Objective: To compare the radiomic features of F-18 fluorodeoxyglucose positron emission tomography/computed tomography (<sup>18</sup>F-FDG PET/CT) and intratumoral heterogeneity according to tumor budding (TB) status and to develop a prediction model for the TB status using the radiomic feature of <sup>18</sup>F-FDG PET/CT in patients with cervical cancer. Materials and Methods: Seventy-six patients with cervical cancer who underwent radical hysterectomy and preoperative <sup>18</sup>F-FDG PET/CT were included. We assessed the status of intratumoral budding (ITP) and peritumoral budding (PTB) in all available hematoxylin and eosin-stained specimens. Three conventional metabolic parameters and fifty-nine features were extracted and analyzed. Univariate analysis was used to identify significant metabolic parameters and radiomic findings for TB status. The prediction model for TB status was built using 3 machine learning classifiers (random forest, support vector machine, and neural network). Results: Univariate analysis led to the identification of 2 significant metabolic parameters and 12 significant radiomic features according to intratumoral budding (ITB) status. Among these parameters, following multivariate analysis for the ITB status, only compacity remained significant (odds ratio, 5.0047; 95% confidence interval, 1.1636–21.5253; <i>p</i> = 0.0305). Two conventional metabolic parameters and 25 radiomic features were selected by the Lasso regularization, and the prediction model for the ITB status had a mean area under the curve of 0.762 in the test dataset. Conclusion: Radiomic features of <sup>18</sup>F-FDG PET/CT were associated with the ITB status. The prediction model using radiomic features successfully predicted the TB status in patients with cervical cancer. The prediction models for the ITB status may contribute to personalized medicine in the management of patients with cervical cancer. |
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| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T08:52:17Z |
| publishDate | 2021-08-01 |
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| series | Diagnostics |
| spelling | doaj.art-11ebe1fec7804a39a5b2210f8c709ee92023-11-22T07:21:39ZengMDPI AGDiagnostics2075-44182021-08-01118151710.3390/diagnostics11081517Prediction Model for Tumor Budding Status Using the Radiomic Features of F-18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Cervical CancerGun Oh Chong0Shin-Hyung Park1Shin Young Jeong2Su Jeong Kim3Nora Jee-Young Park4Yoon Hee Lee5Sang-Woo Lee6Dae Gy Hong7Ji Young Park8Hyung Soo Han9Department of Obstetrics and Gynecology, School of Medicine, Kyungpook National University, Daegu 41944, KoreaDepartment of Radiation Oncology, School of Medicine, Kyungpook National University, Daegu 41944, KoreaDepartment of Nuclear Medicine, School of Medicine, Kyungpook National University, Daegu 41944, KoreaDepartment of Obstetrics and Gynecology, School of Medicine, Kyungpook National University, Daegu 41944, KoreaClinical Omics Research Center, School of Medicine, Kyungpook National University, Daegu 41944, KoreaDepartment of Obstetrics and Gynecology, School of Medicine, Kyungpook National University, Daegu 41944, KoreaDepartment of Nuclear Medicine, School of Medicine, Kyungpook National University, Daegu 41944, KoreaDepartment of Obstetrics and Gynecology, School of Medicine, Kyungpook National University, Daegu 41944, KoreaDepartment of Pathology, School of Medicine, Kyungpook National University, Daegu 41944, KoreaClinical Omics Research Center, School of Medicine, Kyungpook National University, Daegu 41944, KoreaObjective: To compare the radiomic features of F-18 fluorodeoxyglucose positron emission tomography/computed tomography (<sup>18</sup>F-FDG PET/CT) and intratumoral heterogeneity according to tumor budding (TB) status and to develop a prediction model for the TB status using the radiomic feature of <sup>18</sup>F-FDG PET/CT in patients with cervical cancer. Materials and Methods: Seventy-six patients with cervical cancer who underwent radical hysterectomy and preoperative <sup>18</sup>F-FDG PET/CT were included. We assessed the status of intratumoral budding (ITP) and peritumoral budding (PTB) in all available hematoxylin and eosin-stained specimens. Three conventional metabolic parameters and fifty-nine features were extracted and analyzed. Univariate analysis was used to identify significant metabolic parameters and radiomic findings for TB status. The prediction model for TB status was built using 3 machine learning classifiers (random forest, support vector machine, and neural network). Results: Univariate analysis led to the identification of 2 significant metabolic parameters and 12 significant radiomic features according to intratumoral budding (ITB) status. Among these parameters, following multivariate analysis for the ITB status, only compacity remained significant (odds ratio, 5.0047; 95% confidence interval, 1.1636–21.5253; <i>p</i> = 0.0305). Two conventional metabolic parameters and 25 radiomic features were selected by the Lasso regularization, and the prediction model for the ITB status had a mean area under the curve of 0.762 in the test dataset. Conclusion: Radiomic features of <sup>18</sup>F-FDG PET/CT were associated with the ITB status. The prediction model using radiomic features successfully predicted the TB status in patients with cervical cancer. The prediction models for the ITB status may contribute to personalized medicine in the management of patients with cervical cancer.https://www.mdpi.com/2075-4418/11/8/1517cervical cancertumor buddingradiomic features<sup>18</sup>F-FDG PET/CTprediction model |
| spellingShingle | Gun Oh Chong Shin-Hyung Park Shin Young Jeong Su Jeong Kim Nora Jee-Young Park Yoon Hee Lee Sang-Woo Lee Dae Gy Hong Ji Young Park Hyung Soo Han Prediction Model for Tumor Budding Status Using the Radiomic Features of F-18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Cervical Cancer Diagnostics cervical cancer tumor budding radiomic features <sup>18</sup>F-FDG PET/CT prediction model |
| title | Prediction Model for Tumor Budding Status Using the Radiomic Features of F-18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Cervical Cancer |
| title_full | Prediction Model for Tumor Budding Status Using the Radiomic Features of F-18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Cervical Cancer |
| title_fullStr | Prediction Model for Tumor Budding Status Using the Radiomic Features of F-18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Cervical Cancer |
| title_full_unstemmed | Prediction Model for Tumor Budding Status Using the Radiomic Features of F-18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Cervical Cancer |
| title_short | Prediction Model for Tumor Budding Status Using the Radiomic Features of F-18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Cervical Cancer |
| title_sort | prediction model for tumor budding status using the radiomic features of f 18 fluorodeoxyglucose positron emission tomography computed tomography in cervical cancer |
| topic | cervical cancer tumor budding radiomic features <sup>18</sup>F-FDG PET/CT prediction model |
| url | https://www.mdpi.com/2075-4418/11/8/1517 |
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